Fabrizio Abrate

Distributed linear estimation over sensor networks

We consider a network of sensors in which each node may collect noisy linear measurements of some unknown parameter. In this context, we study a distributed consensus diffusion scheme that relies only on bidirectional communication among neighbour nodes (nodes that can communicate and exchange data), and allows every node to compute an estimate of the unknown parameter that asymptotically converges to the true parameter. At each time iteration, a measurement update and a spatial diffusion phase are performed across the network, and a local least-squares estimate is computed at each node. The proposed scheme allows one to consider networks with dynamically changing communication topology, and it is robust to unreliable communication links and failures in measuring nodes. We show that under suitable hypotheses all the local estimates converge to the true parameter value.

An Analytical Packet Error Rate Model for WAVE Receivers

Vehicular Ad-Hoc Networks (VANETs) have been extensively studied in the last years. Despite the availability of IEEE 802.11p transceivers, most of the literature presents simulation results, rather than real-world measurements. This is mainly due to the capability of simulations to facilitate interpretations of results; in fact any event can be decomposed into basic sub-events and the related causes analyzed in depth. However, this requires realistic settings of simulations to lead to consistent conclusions. Nowadays, most of the network simulators neglect the probabilistic nature of the decoding process at the receiver and this may have significant impact on the results. This paper is meant to fill this gap by proposing an analytical Packet Error Rate (PER) model for 802.11preceivers, particularly suitable for the implementation within packet-level simulators. It is completely parameterized as a stochastic function of line-rate, Signal to Noise ratio (SNR) and packet length, and it is consistent with experimental measurements.

Multi-robot Map Updating in Dynamic Environments

Multi-robot systems play an important role in many robotic applications. A prerequisite for a team of robots is the capability of building and maintaining updated maps of the environment. The simultaneous estimation of the trajectory and the map of the environment (known as SLAM) requires many computational resources. Moreover, SLAM is generally performed in environments that do not vary over time (called static environments), whereas real applications commonly require navigation services in dynamic environments. This paper focuses on long term mapping operativity in presence of variations in the map, as in the case of robotic applications in logistic spaces, where rovers have to track the presence of goods in given areas. In this context classical SLAM approaches are generally not directly applicable, since they usually apply in static environments or in dynamic environments where it is possible to model the environment dynamics. This paper proposes a methodology that allows the robots to detect variations in the environment, generate maps containing only the persistent variations, propagate thiem to the team and finally merge the received information in a consistent way. The team of robots is also exploited to assure the coverage of areas not visited for long time, thus improving the knowledge on the present status of the map. The map updating process is demonstrated to be computationally light, in order to be performed in parallel with other tasks (e.g., team coordination and planning, surveillance).

Improvement of WiFi physical model and effects on network simulations

Experimental characterization of small antennas presents a practical problem when the measurement cable is placed in the antenna reactive zone. By changing the current distribution, the cable participates to the radiation process and disturbs the Antenna Under Test (AUT) impedance. The antenna backscattering measurement technique is applied for deriving the input impedance, gain and RCS of cableless electrically small antennas. The backscattered “structural mode” and its role on the results accuracy are analyzed.Simulators are tools for understanding systems and predicting their behavior. They are supposed to be effective, efficient and to trade-off between the level of analytical detail, required to prevent misleading results, and simplicity, meant to keep computational load light. It is fundamental to evaluate the impact of any proposed improvement on the final results when enhancing the simulators. In accordance to these guidelines, this work proposes a novel WiFi physical model which is meant to be an enhancement of network simulators. An analytical model is proposed to adhere more closely to experimental benchmarks on Packet Error Rate (PER) vs Signal-to-Noise Ratio (SNR) for different packet lengths. Network simulations assess the relevance of the proposed model.

Convergence and performance analysis of leaderless synchronization in Wi-Fi networks

This work addresses synchronization in multi-hop wireless access networks, with the main but not exclusive purpose of providing end-to-end Quality of Service by time-based scheduling. In these networks all nodes require a common notion of time in order to deploy a common time reference structure and to locate themselves therein. Although nodes can be synchronized with an external and absolute time reference, as provided by a global navigation satellite system, this work focuses on a distributed synchronization solution exploiting local time information at each node. The convergence and performance analysis of a consensus-based solution called Leaderless Time Synchronization Protocol (LTSP) is presented considering different sources of synchronization errors.

Cooperative robotic teams for supervision and management of large logistic spaces: Methodology and applications

Robots and automated systems can be employed in the logistic field to efficiently perform common tasks like building and updating maps of indoor and outdoor logistic spaces, locating specific goods on the map, tracing the product flow in the area, while assuring the surveillance of the environment. This paper reports and discusses the already achieved results of the on-going research project MACP4Log (Mobile Autonomous and Cooperating robotic Platforms for supervision and monitoring of large LOGistic surfaces), aimed at the study and development of a prototype of a mobile robotic platform, with on-board vision systems and sensors, integrating a flexible wireless communication solution, able to move autonomously in large logistic spaces, and to communicate with a supervisor and other similar platforms in order to achieve a coordinated action to carry out specific tasks.

Monte Carlo Localization of mini-rovers with low-cost IR sensors

The localization problem is one of the most important and investigated problems in mobile robotics. A great number of solutions have been described in many papers and many different theoretical approaches have been presented. Among the sampling-based approaches Monte Carlo Localization (MCL) exploits the Monte Carlo techniques to solve the localization problem. The MCL approach has been intensively tested on problems involving robots equipped with range sensors such as sonar or laser sensors leading to very good performances. In this paper the application of MCL techniques to the localization of mobile mini-robots, equipped with only low-cost Infra-Red (IR) sensors, is investigated and discussed both in simulation and experimentally.

Hybrid localization solutions for robotic logistic applications

This paper presents some results concerning rovers localization, achieved within the framework of the Italian Regional project MACP4Log [1]. This project investigates the coordination of a team of rovers that delivers assistance services in the context of logistic spaces. The issue of single rover localization is a service prerequisite which is faced by different approaches. First a WiFi-based localization technique is examined: it involves building a radio map of the Received Signal Strength (RSS) in the considered environment and, afterwards, setting the problem of localization as an optimization problem: given a RSS, the function is reversed to get the estimated position of the rover. Finally the proposed WiFi technique is integrated with a particle filter algorithm following both a “loose” and a “tight” coupling approaches, integrating radio information at two different levels. Experimental results in monodimensional and bidimensional environments validate these approaches and allow a comparative analysis among them.

Efficient solutions for programming and safe monitoring of an industrial robot via a virtual cell

In the short- and mid-term visions for robotics development, robots are expected to be used also in small and medium-sized companies, sharing spaces with different manufacturing cycles and possibly with human operators. This objective can be fulfilled only assuring high flexibility in production control, safety for workers and for machinery, and interfaces that allow an easy use of the equipment. The development of a proper hardware/software architecture for robot motion planning and on-line safe monitoring is a fundamental step, which requires the solution of various problems, both from the theoretical and the practical point of view. A hardware/software architecture has been developed using Microsoft Robotics Developers Studio and implemented for a six-dof COMAU NS 12 robot, allowing in particular to dynamically constrain the robot motion, and to supervise it on-line. This paper develops (i) a fast and efficient procedure to perform iterative inverse kinematics, in order to achieve full programming capabilities in the Cartesian space, and (ii) safety solutions that avoid the robot to enter the off-limits regions of the workspace, considering the delays between the motion of the real robot and its replica in the virtual cell.

Distributed Maximum Likelihood Estimation over Unreliable Sensor Networks

In this chapter we consider a network of sensing nodes where each sensor may take at each time iteration a noisy linear measurement of some unknown parameter that we wish to estimate. We study a distributed consensus diffusion scheme, which allows every node to compute an estimate of the unknown parameter that asymptotically converges to the “true” parameter value. The diffusion scheme relies only on bidirectional communication among neighboring nodes. A measurement update and a network diffusion phase are performed across the network at each iteration, and then each node computes a local least-squares estimate of the unknown parameter. We prove that the local estimates converge to the true parameter value, under suitable hypotheses. The proposed scheme works on networks with dynamically changing communication topology, thus being robust to unreliable communication links and failures in measuring nodes.